In the oil and gas industry today, there are several conditions that drive the need for non-traditional methods for obtaining open hole logging data. As a result, oil and gas companies are more inclined to explore such non-traditional methods for obtaining open hole logging data to help in their decision-making processes. The use of cased hole logging data, in particular pulsed neutron data, to generate pseudo or artificial open hole triple combo log information is one approach which has been found to be useful.
One of the conditions is simple economics. Every operation carried out in a borehole takes time, which translates directly to increased cost of drilling the well. Therefore, if a logging operation in the well, e.g. an open hole log, can be avoided, it reduces the cost of drilling the well. If the same data can be obtained from another operation which will normally be performed in any case, e.g. a cased hole pulsed neutron log, then the actual open hole log can be skipped, saving time and money.
Adverse drilling conditions often make open hole logging expensive, risky or essentially impossible. Such conditions include extreme wash outs, shale bridges, caving, etc. These conditions may make it physically impossible to run an open hole logging tool in the hole. If the tool can be run, the conditions may prevent collection of useful data in at least portions of the well.
Modern drilling techniques may make open hole logging risky or impossible. For example, highly deviated wells may have high rates of turn or high angles which make it difficult or impossible to run an open hole tool. Some companies use slim holes, e.g. 3.5 inch diameter wells, which are too small for available open hole logging tools. However, pulsed neutron logging tools are available for running in such wells after they are cased.
Most geologists and engineers are familiar with data produced by open hole logs, e.g. the resistivity and porosity curves. They are accustomed to using such data to calculate saturation. However, they are not familiar with the curves produced by pulsed neutron logs. As a result of these conditions, efforts have been made to produce synthetic or artificial open hole type logs from real data taken by pulsed neutron logging tools. However, various difficulties have been encountered in developing the predictive tools or models which are used to create such synthetic logs. For this approach to be successful, the models must produce accurate synthetic logs which can be relied on.
Various predictive tools have been used in processing geological logging data for many years. A field data based predictive model usually takes selected measurements of specific logging tools as inputs and produces predicted outputs using either a deterministic function or an empirical function generated from a training process. As a typical predictive framework, the artificial neural network (ANN) has been used in petrophysical applications. Actual pulsed neutron data and open hole data from a selected well may be used to build an ANN model using optimization algorithms. The trained ANN may then be tested with data from other parts of the same well or from different wells for validation.
Systems using a single neural network trained in this way are capable of providing good synthetic or artificial triple combo open hole logs from real data taken by pulsed neutron logging tools, at least for wells near, or in the same geological area as, the well or wells used for training. However, neural network ensembles have been found better suited for large and complex cases and are capable of better generalization performance. A neural network ensemble is a set or group of several individual neural networks, each of which have been trained to operate on the same set of input parameters and produce a predicted or synthetic output of one or more other parameters. For the well logging example discussed herein, each neural network is trained to operate on seven input parameters from a pulsed neutron logging tool and generate three synthetic output parameters corresponding to the outputs of a triple combo open hole logging tool. The outputs of the ensemble members are combined, e.g. averaged, to produce a more accurate synthetic output or prediction.
A properly selected ensemble of trained neural networks has been shown to have a better generalization performance than a typical single network. However, since the conventional selection of a neural network ensemble includes manually determining the number of networks in the ensemble, the structure of the individual networks, and the weighting coefficients of the individual networks, it often involves a tedious trial and error process which may be difficult for inexperienced users.
Some automated processes for selecting neural network ensembles are known, for example genetic algorithms. However, such processes require selection of an objective function for the network selection. A typical objective function would be the generalization error of each network, that is the error measured over all possible inputs. Since all possible inputs are not available, it is not possible to know the actual generalization error. The normal practice is to minimize the validation error based on the available data. A problem arises if the validation data set is not a good representation of the unknown new data due to its limited availability or diversity. This problem may occur when the validation data is selected from only one well and the neural network ensemble is expected to interpret data from other wells, which is normally the intended use of the ensemble.
One reason neural network ensembles provide better results than a single neural network is diversity or negative correlation of errors generated by the neural networks forming an ensemble. Diversity has been achieved by various techniques, e.g. using networks with different topologies or different starting coefficients, or using different data to train the networks. More recently a technique known as negative correlation learning has been used in the network training process to increase diversity among networks. However, the known algorithm is a stochastic approach, i.e. the network coefficients and architectures are updated pattern by pattern. It cannot be used in batch mode training in conventional neural network software environments.